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BACKGROUND Conscious states are typically inferred through responses to auditory tasks and noxious stimulation. We report the use of a stimulus-free behavioural paradigm to track state transitions in responsiveness during dexmedetomidine sedation. We hypothesised that estimated dexmedetomidine effect-site (Ce) concentrations would be higher at loss of responsiveness (LOR) compared with return of responsiveness (ROR), and both would be lower than comparable studies that used stimulus-based assessments. METHODS Closed-Loop Acoustic Stimulation during Sedation with Dexmedetomidine data were analysed for secondary analysis. Fourteen healthy volunteers were asked to perform the breathe-squeeze task of gripping a dynamometer when inspiring and releasing it when expiring. LOR was defined as five inspirations without accompanied squeezes; ROR was defined as the return of five inspirations accompanied by squeezes. Brain states were monitored using 64-channel EEG. Dexmedetomidine was administered as a target-controlled infusion, with Ce estimated from a pharmacokinetic model. RESULTS Counter to our hypothesis, mean estimated dexmedetomidine Ce was lower at LOR (0.92 ng ml-1; 95% confidence interval: 0.69-1.15) than at ROR (1.43 ng ml-1; 95% confidence interval: 1.27-1.58) (paired t-test; P=0.002). LOR was characterised by progressively increasing fronto-occipital EEG power in the 0.5-8 Hz band and loss of occipital alpha (8-12 Hz) and global beta (16-30 Hz) power. These EEG changes reverted at ROR. CONCLUSIONS The breathe-squeeze task can effectively track changes in responsiveness during sedation without external stimuli and might be more sensitive to state changes than stimulus-based tasks. It should be considered when perturbation of brain states is undesirable. CLINICAL TRIAL REGISTRATION NCT04206059.

Background: Cognitive abilities decline with aging, leading to a higher risk for the development of postoperative delirium or postoperative neurocognitive disorders after general anesthesia. Since frontal α-band power is known to be highly correlated with cognitive function in general, we hypothesized that preoperative cognitive impairment is associated with lower baseline and intraoperative frontal α-band power in older adults. Methods: Patients aged ≥65 years undergoing elective surgery were included in this prospective observational study. Cognitive function was assessed on the day before surgery using six age-sensitive cognitive tests. Scores on those tests were entered into a principal component analysis to calculate a composite "g score" of global cognitive ability. Patient groups were dichotomized into a lower cognitive group (LC) reaching the lower 1/3 of "g scores" and a normal cognitive group (NC) consisting of the upper 2/3 of "g scores." Continuous pre- and intraoperative frontal electroencephalograms (EEGs) were recorded. EEG spectra were analyzed at baseline, before start of anesthesia medication, and during a stable intraoperative period. Significant differences in band power between the NC and LC groups were computed by using a frequency domain (δ0.5-3 Hz, θ 4-7 Hz, α 8-12 Hz, β 13-30 Hz)-based bootstrapping algorithm. Results: Of 38 included patients (mean age 72 years), 24 patients were in the NC group, and 14 patients had lower cognitive abilities (LC). Intraoperative α-band power was significantly reduced in the LC group compared to the NC group (NC -1.6 [-4.48/1.17] dB vs. LC -6.0 [-9.02/-2.64] dB), and intraoperative α-band power was positively correlated with "g score" (Spearman correlation: r = 0.381; p = 0.018). Baseline EEG power did not show any associations with "g." Conclusions: Preoperative cognitive impairment in older adults is associated with intraoperative absolute frontal α-band power, but not baseline α-band power., Seventh Framework Programme (European Commission) (FP7/2007-2013) (Grant HEALTH-F2-2014-60246)

The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable experimental conditions occasionally results in insufficient data collection due to factors such as low neuronal firing rates or brief recording periods, and in these cases, the standard maximum likelihood estimate becomes unreliable. The present studies compares the performance of different statistical inference procedures when applied to the estimation of functional connectivity in neuronal assemblies with sparse spiking data. Four inference methods were compared: maximum likelihood estimation, penalized maximum likelihood estimation, using either l2 or l1 regularization, and hierarchical Bayesian estimation based on a variational Bayes algorithm. Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor cortex. The identification of spiking dependencies in physiologically acquired data was encouraging, since their sparse nature would have previously precluded them from successful analysis using traditional methods., National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant Grant R01-DA015644), National Institutes of Health (U.S.) (Grant Grant R01-HL084502

Behavioral state is known to influence interactions between thalamus and cortex, which are important for sensation, action, and cognition. The thalamic reticular nucleus (TRN) is hypothesized to regulate thalamo-cortical interactions, but the underlying functional architecture of this process and its state dependence are unknown. By combining the first TRN ensemble recording with psychophysics and connectivity-based optogenetic tagging, we found reticular circuits to be composed of distinct subnetworks. While activity of limbic-projecting TRN neurons positively correlates with arousal, sensory-projecting neurons participate in spindles and show elevated synchrony by slow waves during sleep. Sensory-projecting neurons are suppressed by attentional states, demonstrating that their gating of thalamo-cortical interactions is matched to behavioral state. Bidirectional manipulation of attentional performance was achieved through subnetwork-specific optogenetic stimulation. Together, our findings provide evidence for differential inhibition of thalamic nuclei across brain states, where the TRN separately controls external sensory and internal limbic processing facilitating normal cognitive function., National Institute of Neurological Disorders and Stroke (U.S.) (NIH Pathway to Independence Career Award K99 NS 078115), Brain & Behavior Research Foundation (Young Investigator Award), National Institutes of Health (U.S.) ( Transformative R01 Award TR01-GM10498), National Institutes of Health (U.S.) (Grant R01-MH061976)

MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-posed inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be improved by incorporating models of these dynamics. In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent with neurophysiology and neuroanatomy. We develop a dynamic Maximum a Posteriori Expectation-Maximization (dMAP-EM) source localization algorithm for estimation of cortical sources and model parameters based on the Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the dMAP-EM algorithm to simulated experiments as well as to human experimental data. Furthermore, we derive expressions to relate our dynamic estimation formulas to those of standard static models, and show how dynamic methods optimally assimilate past and future data. Our results establish the feasibility of spatiotemporal dynamic estimation in large-scale distributed source spaces with several thousand source locations and hundreds of sensors, with resulting inverse solutions that provide substantial performance improvements over static methods.

AIM: In order to successfully detect, classify, prognosticate, and develop targeted therapies for patients with disorders of consciousness (DOC), it is crucial to improve our mechanistic understanding of how severe brain injuries result in these disorders. METHODS: To address this need, the Curing Coma Campaign convened a Mechanisms Sub-Group of the Coma Science Work Group (CSWG), aiming to identify the most pressing knowledge gaps and the most promising approaches to bridge them. RESULTS: We identified a key conceptual gap in the need to differentiate the neural mechanisms of consciousness per se, from those underpinning connectedness to the environment and behavioral responsiveness. Further, we characterised three fundamental gaps in DOC research: (1) a lack of mechanistic integration between structural brain damage and abnormal brain function in DOC; (2) a lack of translational bridges between micro- and macro-scale neural phenomena; and (3) an incomplete exploration of possible synergies between data-driven and theory-driven approaches. CONCLUSION: In this white paper, we discuss research priorities that would enable us to begin to close these knowledge gaps. We propose that a fundamental step towards this goal will be to combine translational, multi-scale, and multimodal data, with new biomarkers, theory-driven approaches, and computational models, to produce an integrated account of neural mechanisms in DOC. Importantly, we envision that reciprocal interaction between domains will establish a "virtuous cycle," leading towards a critical vantage point of integrated knowledge that will enable the advancement of the scientific understanding of DOC and consequently, an improvement of clinical practice., This work was supported by the Tiny Blue Dot Foundation; the Templeton World Charity Foundation, Inc.; grant NInDS K23NS112473; NIH-NIGMS Grant R01GM135420; the National Institute of General Medical Sciences (1K08GM121961); The Johns Hopkins University Discovery Award, Stimulating and Advancing ACCM Research Award; the European Union���s Horizon 2020 Framework Program for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3); the Canadian Institute for Advanced Research (CIFAR) (RCZB/072 RG93193); the Stephen Erskine Fellowship (Queens��� College, Cambridge, UK); the Cambridge European Trust; the Gates Cambridge Trust.

Objectives Switching from maintenance of general anesthesia with an ether anesthetic to maintenance with high-dose (concentration >50% and total gas flow rate >4 liters per minute) nitrous oxide is a common practice used to facilitate emergence from general anesthesia. The transition from the ether anesthetic to nitrous oxide is associated with a switch in the putative mechanisms and sites of anesthetic action. We investigated whether there is an electroencephalogram (EEG) marker of this transition. Methods We retrospectively studied the ether anesthetic to nitrous oxide transition in 19 patients with EEG monitoring receiving general anesthesia using the ether anesthetic sevoflurane combined with oxygen and air. Results Following the transition to nitrous oxide, the alpha (8–12 Hz) oscillations associated with sevoflurane dissipated within 3–12 min (median 6 min) and were replaced by highly coherent large-amplitude slow-delta (0.1–4 Hz) oscillations that persisted for 2–12 min (median 3 min). Conclusions Administration of high-dose nitrous oxide is associated with transient, large amplitude slow-delta oscillations. Significance We postulate that these slow-delta oscillations may result from nitrous oxide-induced blockade of major excitatory inputs (NMDA glutamate projections) from the brainstem (parabrachial nucleus and medial pontine reticular formation) to the thalamus and cortex. This EEG signature of high-dose nitrous oxide may offer new insights into brain states during general anesthesia., National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant TR01-GM104948)

The widely used electroencephalogram-based indices for depth-of-Anesthesia monitoring assume that the same index value defines the same level of unconsciousness for all anesthetics. In contrast, we show that different anesthetics act at different molecular targets and neural circuits to produce distinct brain states that are readily visible in the electroencephalogram. We present a two-part review to educate anesthesiologists on use of the unprocessed electroencephalogram and its spectrogram to track the brain states of patients receiving anesthesia care. Here in part I, we review the biophysics of the electroencephalogram and the neurophysiology of the electroencephalogram signatures of three intravenous anesthetics: propofol, dexmedetomidine, and ketamine, and four inhaled anesthetics: sevoflurane, isoflurane, desflurane, and nitrous oxide. Later in part II, we discuss patient management using these electroencephalogram signatures. Use of these electroencephalogram signatures suggests a neurophysiologically based paradigm for brain state monitoring of patients receiving anesthesia care., National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant TR01-GM104948)

Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent developments in source localization methodology have emphasized temporal as well as spatial constraints to improve source localization accuracy, but these methods can be computationally intense. Solutions emphasizing spatial sparsity hold tremendous promise, since the underlying neurophysiological processes generating MEG signals are often sparse in nature, whether in the form of focal sources, or distributed sources representing large-scale functional networks. Recent developments in the theory of compressed sensing (CS) provide a rigorous framework to estimate signals with sparse structure. In particular, a class of CS algorithms referred to as greedy pursuit algorithms can provide both high recovery accuracy and low computational complexity. Greedy pursuit algorithms are difficult to apply directly to the MEG inverse problem because of the high-dimensional structure of the MEG source space and the high spatial correlation in MEG measurements. In this paper, we develop a novel greedy pursuit algorithm for sparse MEG source localization that overcomes these fundamental problems. This algorithm, which we refer to as the Subspace Pursuit-based Iterative Greedy Hierarchical (SPIGH) inverse solution, exhibits very low computational complexity while achieving very high localization accuracy. We evaluate the performance of the proposed algorithm using comprehensive simulations, as well as the analysis of human MEG data during spontaneous brain activity and somatosensory stimuli. These studies reveal substantial performance gains provided by the SPIGH algorithm in terms of computational complexity, localization accuracy, and robustness., National Institutes of Health (U.S.) (New Innovator Award DP2-OD006454), National Institutes of Health (U.S.) (R01-EB006385-01), National Institutes of Health (U.S.) (P41-RR014075-11)

PSD-95-like, disc-large (DLG) family membrane-associated guanylate kinase proteins (PSD/DLG-MAGUKs) are essential for regulating synaptic AMPA receptor (AMPAR) function and activity-dependent trafficking of AMPARs. Using a molecular replacement strategy to replace endogenous PSD-95 with SAP97β, we show that the prototypic β-isoform of the PSD-MAGUKs, SAP97β, has distinct NMDA receptor (NMDAR)-dependent roles in regulating basic properties of AMPAR-containing synapses. SAP97β enhances the number of AMPAR-containing synapses in an NMDAR-dependent manner, whereas its effect on the size of unitary synaptic response is not fully dependent on NMDAR activity. These effects contrast with those of PSD-95α, which increases both the number of AMPAR-containing synapses and the size of unitary synaptic responses, with or without NMDAR activity. Our results suggest that SAP97β regulates synaptic AMPAR content by increasing surface expression of GluA1-containing AMPARs, whereas PSD-95α enhances synaptic AMPAR content presumably by increasing the synaptic scaffold capacity for synaptic AMPARs. Our approach delineates discrete effects of different PSD-MAGUKs on principal properties of glutamatergic synaptic transmission. Our results suggest that the molecular diversity of PSD-MAGUKs can provide rich molecular substrates for differential regulation of glutamatergic synapses in the brain., National Institutes of Health (U.S.) (Grant MH080310), National Institutes of Health (U.S.) (Transformative Research Award R01 GM104948-02)

Burst suppression is an electroencephalogram pattern that consists of a quasi-periodic alternation between isoelectric ‘suppressions’ lasting seconds or minutes, and high-voltage ‘bursts’. It is characteristic of a profoundly inactivated brain, occurring in conditions including hypothermia, deep general anaesthesia, infant encephalopathy and coma. It is also used in neurology as an electrophysiological endpoint in pharmacologically induced coma for brain protection after traumatic injury and during status epilepticus. Classically, burst suppression has been regarded as a ‘global’ state with synchronous activity throughout cortex. This assumption has influenced the clinical use of burst suppression as a way to broadly reduce neural activity. However, the extent of spatial homogeneity has not been fully explored due to the challenges in recording from multiple cortical sites simultaneously. The neurophysiological dynamics of large-scale cortical circuits during burst suppression are therefore not well understood. To address this question, we recorded intracranial electrocorticograms from patients who entered burst suppression while receiving propofol general anaesthesia. The electrodes were broadly distributed across cortex, enabling us to examine both the dynamics of burst suppression within local cortical regions and larger-scale network interactions. We found that in contrast to previous characterizations, bursts could be substantially asynchronous across the cortex. Furthermore, the state of burst suppression itself could occur in a limited cortical region while other areas exhibited ongoing continuous activity. In addition, we found a complex temporal structure within bursts, which recapitulated the spectral dynamics of the state preceding burst suppression, and evolved throughout the course of a single burst. Our observations imply that local cortical dynamics are not homogeneous, even during significant brain inactivation. Instead, cortical and, implicitly, subcortical circuits express seemingly different sensitivities to high doses of anaesthetics that suggest a hierarchy governing how the brain enters burst suppression, and emphasize the role of local dynamics in what has previously been regarded as a global state. These findings suggest a conceptual shift in how neurologists could assess the brain function of patients undergoing burst suppression. First, analysing spatial variation in burst suppression could provide insight into the circuit dysfunction underlying a given pathology, and could improve monitoring of medically-induced coma. Second, analysing the temporal dynamics within a burst could help assess the underlying brain state. This approach could be explored as a prognostic tool for recovery from coma, and for guiding treatment of status epilepticus. Overall, these results suggest new research directions and methods that could improve patient monitoring in clinical practice., Burroughs Wellcome Fund (Career Award at the Scientific Interface), National Institutes of Health (U.S.) (Director's Pioneer Award DP10D003646), National Institutes of Health (U.S.) (Transformative 1R01GM104948)

MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be improved by incorporating models of these dynamics. In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent with neurophysiology and neuroanatomy. We develop a dynamic Maximum a Posteriori Expectation-Maximization (dMAP-EM) source localization algorithm for estimation of cortical sources and model parameters based on the Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the dMAP-EM algorithm to simulated experiments as well as to human experimental data. Furthermore, we derive expressions to relate our dynamic estimation formulas to those of standard static models, and show how dynamic methods optimally assimilate past and future data. Our results establish the feasibility of spatiotemporal dynamic estimation in large-scale distributed source spaces with several thousand source locations and hundreds of sensors, with resulting inverse solutions that provide substantial performance improvements over static methods.

Over the last decade there has been a tremendous advance in the analytical tools available to neuroscientists to understand and model neural function. In particular, the point process – generalized linear model (PP-GLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT – an open source neural spike train analysis toolbox for Matlab[superscript ®]. By adopting an object-oriented programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of peri-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems., National Institutes of Health (U.S.) (F31NS058275)

Adenosine-to-inosine (A-to-I) RNA editing is a neurodevelopmentally regulated epigenetic modification shown to modulate complex behavior in animals. Little is known about human A-to-I editing, but it is thought to constitute one of many molecular mechanisms connecting environmental stimuli and behavioral outputs. Thus, comprehensive exploration of A-to-I RNA editing in human brains may shed light on gene–environment interactions underlying complex behavior in health and disease. Synaptic function is a main target of A-to-I editing, which can selectively recode key amino acids in synaptic genes, directly altering synaptic strength and duration in response to environmental signals. Here, we performed a high-resolution survey of synaptic A-to-I RNA editing in a human population, and examined how it varies in autism, a neurodevelopmental disorder in which synaptic abnormalities are a common finding. Using ultra-deep (>1000 × ) sequencing, we quantified the levels of A-to-I editing of 10 synaptic genes in postmortem cerebella from 14 neurotypical and 11 autistic individuals. A high dynamic range of editing levels was detected across individuals and editing sites, from 99.6% to below detection limits. In most sites, the extreme ends of the population editing distributions were individuals with autism. Editing was correlated with isoform usage, clusters of correlated sites were identified, and differential editing patterns examined. Finally, a dysfunctional form of the editing enzyme adenosine deaminase acting on RNA B1 was found more commonly in postmortem cerebella from individuals with autism. These results provide a population-level, high-resolution view of A-to-I RNA editing in human cerebella and suggest that A-to-I editing of synaptic genes may be informative for assessing the epigenetic risk for autism., Nancy Lurie Marks Family Foundation, F. Hoffmann-La Roche & Co. (Applied Science Sequencing Grant Program), Autism Speaks (Organization), Simons Foundation, National Institutes of Health (U.S.) (Grant 1R01MH085143-01)

Multichannel electroencephalography (EEG) offers a non-invasive tool to explore spatio-temporal dynamics of brain activity. With EEG recordings consisting of multiple trials, traditional signal processing approaches that ignore inter-trial variability in the data may fail to accurately estimate the underlying spatio-temporal brain patterns. Moreover, precise characterization of such inter-trial variability per se can be of high scientific value in establishing the relationship between brain activity and behavior. In this paper, a statistical modeling framework is introduced for learning spatio-temporal decompositions of multiple-trial EEG data recorded under two contrasting experimental conditions. By modeling the variance of source signals as random variables varying across trials, the proposed two-stage hierarchical Bayesian model is able to capture inter-trial amplitude variability in the data in a sparse way where a parsimonious representation of the data can be obtained. A variational Bayesian (VB) algorithm is developed for statistical inference of the hierarchical model. The efficacy of the proposed modeling framework is validated with the analysis of both synthetic and real EEG data. In the simulation study we show that even at low signal-to-noise ratios our approach is able to recover with high precision the underlying spatio-temporal patterns and the dynamics of source amplitude across trials; on two brain–computer interface (BCI) data sets we show that our VB algorithm can extract physiologically meaningful spatio-temporal patterns and make more accurate predictions than other two widely used algorithms: the common spatial patterns (CSP) algorithm and the Infomax algorithm for independent component analysis (ICA). The results demonstrate that our statistical modeling framework can serve as a powerful tool for extracting brain patterns, characterizing trial-to-trial brain dynamics, and decoding brain states by exploiting useful structures in the data., National Institutes of Health (U.S.) (Grant DP1-OD003646-01), National Institutes of Health (U.S.) (Grant R01-EB006385-01), National Natural Science Foundation (China) (Grant 30630022)

We propose a point process model of spiking activity from auditory neurons. The model takes account of the neuron’s intrinsic dynamics as well as the spectro-temporal properties of an input stimulus. A discrete Volterra expansion is used to derive the form of the conditional intensity function. The Volterra expansion models the neuron’s baseline spike rate, its intrinsic dynamics - spiking history - and the stimulus effect which in this case is the analog of the spectro-temporal receptive field (STRF). We performed the model fitting efficiently in a generalized linear model framework using ridge regression to address properly this ill-posed maximum likelihood estimation problem. The model provides an excellent fit to spiking activity from 55 auditory nerve neurons. The STRF-like representation estimated jointly with the neuron’s intrinsic dynamics may offer more accurate characterizations of neural activity in the auditory system than current ones based solely on the STRF.

It has been long appreciated that anesthetic drugs induce stereotyped changes in electroencephalogram (EEG), but the relationships between the EEG and underlying brain function remain poorly understood. Functional imaging methods including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), have become important tools for studying how anesthetic drugs act in the human brain to induce the state of general anesthesia. To date, no investigation has combined functional MRI with EEG to study general anesthesia. We report here a paradigm for conducting combined fMRI and EEG studies of human subjects under general anesthesia. We discuss the several technical and safety problems that must be solved to undertake this type of multimodal functional imaging and show combined recordings from a human subject. Combined fMRI and EEG exploits simultaneously the high spatial resolution of fMRI and the high temporal resolution of EEG. In addition, combined fMRI and EEG offers a direct way to relate established EEG patterns induced by general anesthesia to changes in neural activity in specific brain regions as measured by changes in fMRI blood oxygen level dependent (BOLD) signals., National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant K25-NS05758), National Institutes of Health (U.S.) (Grant M01-RR-01066), National Institutes of Health (U.S.) (Grant RR025758- 01), Massachusetts General Hospital. Dept. of Anesthesia and Critical Care

In tasks of associative learning, animals establish new links between unrelated items by using information about trial outcome to strengthen correct/rewarded associations and modify incorrect/unrewarded ones. To study how hippocampal neurons convey information about reward and trial outcome during new associative learning, we recorded hippocampal neurons as monkeys learned novel object-place associations. A large population of hippocampal neurons (50%) signaled trial outcome by differentiating between correct and error trials during the period after the behavioral response. About half these cells increased their activity following correct trials (correct up cells) while the remaining half fired more following error trials (error up cells). Moreover, correct up cells, but not error up cells, conveyed information about learning by increasing their stimulus-selective response properties with behavioral learning. These findings suggest that information about successful trial outcome conveyed by correct up cells may influence new associative learning through changes in the cell's stimulus-selective response properties., National Institutes of Health (U.S.) (NIH grant MH48847), National Institutes of Health (U.S.) (NIH Award DA015644), National Institutes of Health (U.S.) (NIH Award MH59733), National Institutes of Health (U.S.) (NIH grant MH071847), National Institutes of Health (U.S.) (NIH grant DP1 OD003646), Fondation pour la recherche médicale

Objective. Ketamine is a widely used drug with clinical and research applications, and also known to be used as a recreational drug. Ketamine produces conspicuous changes in the electrocorticographic (ECoG) signals observed both in humans and rodents. In rodents, the intracranial ECoG displays a high-frequency oscillation (HFO) which power is modulated nonlinearly by ketamine dose. Despite the widespread use of ketamine there is no model description of the relationship between the pharmacokinetic–pharmacodynamics (PK–PDs) of ketamine and the observed HFO power. Approach. In the present study, we developed a PK–PD model based on estimated ketamine concentration, its known pharmacological actions, and observed ECoG effects. The main pharmacological action of ketamine is antagonism of the NMDA receptor (NMDAR), which in rodents is accompanied by an HFO observed in the ECoG. At high doses, however, ketamine also acts at non-NMDAR sites, produces loss of consciousness, and the transient disappearance of the HFO. We propose a two-compartment PK model that represents the concentration of ketamine, and a PD model based in opposing effects of the NMDAR and non-NMDAR actions on the HFO power. Main results. We recorded ECoG from the cortex of rats after two doses of ketamine, and extracted the HFO power from the ECoG spectrograms. We fit the PK–PD model to the time course of the HFO power, and showed that the model reproduces the dose-dependent profile of the HFO power. The model provides good fits even in the presence of high variability in HFO power across animals. As expected, the model does not provide good fits to the HFO power after dosing the pure NMDAR antagonist MK-801. Significance. Our study provides a simple model to relate the observed electrophysiological effects of ketamine to its actions at the molecular level at different concentrations. This will improve the study of ketamine and rodent models of schizophrenia to better understand the wide and divergent range of effects that ketamine has., National Institutes of Health (U.S.) (Pioneer Award DP1-OD003646), Burroughs Wellcome Fund (Career Award at the Scientific Interface), National Institutes of Health (U.S.) (Grant 5R01MH061976), National Institutes of Health (U.S.) (New Innovator Award DP2-OD006454)

Cortisol is released to relay information to cells to regulate metabolism and reaction to stress and inflammation. In particular, cortisol is released in the form of pulsatile signals. This low-energy method of signaling seems to be more efficient than continuous signaling. We hypothesize that there is a controller in the anterior pituitary that leads to pulsatile release of cortisol, and propose a mathematical formulation for such controller, which leads to impulse control as opposed to continuous control. We postulate that this controller is minimizing the number of secretory events that result in cortisol secretion, which is a way of minimizing the energy required for cortisol secretion; this controller maintains the blood cortisol levels within a specific circadian range while complying with the first order dynamics underlying cortisol secretion. We use an ℓ[subscript 0]-norm cost function for this controller, and solve a reweighed ℓ[subscript 1[-norm minimization algorithm for obtaining the solution to this optimization problem. We use four examples to illustrate the performance of this approach: (i) a toy problem that achieves impulse control, (ii) two examples that achieve physiologically plausible pulsatile cortisol release, (iii) an example where the number of pulses is not within the physiologically plausible range for healthy subjects while the cortisol levels are within the desired range. This novel approach results in impulse control where the impulses and the obtained blood cortisol levels have a circadian rhythm and an ultradian rhythm that are in agreement with the known physiology of cortisol secretion. The proposed formulation is a first step in developing intermittent controllers for curing cortisol deficiency. This type of bio-inspired pulse controllers can be employed for designing non-continuous controllers in brain-machine interface design for neuroscience applications., National Science Foundation (U.S.). Graduate Research Fellowship, National Institutes of Health (U.S.) (DP1 OD003646), National Institutes of Health (U.S.) (1-R01-GM104948-03), National Science Foundation (U.S.) (0836720), National Science Foundation (U.S.) (EFRI-0735956)

Methylphenidate induces reanimation (active emergence) from general anesthesia in rodents, and recent evidence suggests that dopaminergic neurotransmission is important in producing this effect. Dextroamphetamine causes the direct release of dopamine and norepinephrine, whereas atomoxetine is a selective reuptake inhibitor for norepinephrine. Like methylphenidate, both drugs are prescribed to treat Attention Deficit Hyperactivity Disorder. In this study, we tested the efficacy of dextroamphetamine and atomoxetine for inducing reanimation from general anesthesia in rats. Emergence from general anesthesia was defined by return of righting. During continuous sevoflurane anesthesia, dextroamphetamine dose-dependently induced behavioral arousal and restored righting, but atomoxetine did not (n = 6 each). When the D1 dopamine receptor antagonist SCH-23390 was administered prior to dextroamphetamine under the same conditions, righting was not restored (n = 6). After a single dose of propofol (8 mg/kg IV), the mean emergence times for rats that received normal saline (vehicle) and dextroamphetamine (1 mg/kg IV) were 641 sec and 404 sec, respectively (n = 8 each). The difference was statistically significant. Although atomoxetine reduced mean emergence time to 566 sec (n = 8), this decrease was not statistically significant. Spectral analysis of electroencephalogram recordings revealed that dextroamphetamine and atomoxetine both induced a shift in peak power from δ (0.1–4 Hz) to θ (4–8 Hz) during continuous sevoflurane general anesthesia, which was not observed when animals were pre-treated with SCH-23390. In summary, dextroamphetamine induces reanimation from general anesthesia in rodents, but atomoxetine does not induce an arousal response under the same experimental conditions. This supports the hypothesis that dopaminergic stimulation during general anesthesia produces a robust behavioral arousal response. In contrast, selective noradrenergic stimulation causes significant neurophysiological changes, but does not promote behavioral arousal during general anesthesia. We hypothesize that dextroamphetamine is more likely than atomoxetine to be clinically useful for restoring consciousness in anesthetized patients, mainly due to its stimulation of dopaminergic neurotransmission., National Institute of General Medical Sciences (U.S.) (Grant TR01-GM104948)

Burst suppression is an EEG pattern characterized by alternating periods of high-amplitude activity (bursts) and relatively low amplitude activity (suppressions). Burst suppression can arise from several different pathological conditions, as well as from general anesthesia. Here we review current algorithms that are used to quantify burst suppression, its various etiologies, and possible underlying mechanisms. We then review clinical applications of anesthetic-induced burst suppression. Finally, we report the results of our new study showing clear electrophysiological differences in burst suppression patterns induced by two common general anesthetics, sevoflurane and propofol. Our data suggest that the circuit mechanisms that generate burst suppression activity may differ among general anesthetics., National Institutes of Health (U.S.) (Grant TR01-GM104948), National Institutes of Health (U.S.) (Grant K08-GM094394)

Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems., United States. Dept. of Defense (USAMRAA Cooperative Agreement W81XWH-09-2-0001), United States. Dept. of Veteran Affairs (B6453R), United States. Dept. of Veteran Affairs (A6779I), United States. Dept. of Veteran Affairs (B6310N), United States. Dept. of Veteran Affairs (B6459L), National Institutes of Health (U.S.) (R01 DC009899), National Institutes of Health (U.S.) (RC1 HD063931), National Institutes of Health (U.S.) (N01 HD053403), National Institutes of Health (U.S.) (DP1 OD003646), National Institutes of Health (U.S.) (TR01 GM104948), National Science Foundation (U.S.) (CBET 1159652), Wings for Life Spinal Cord Research Foundation, Doris Duke Foundation, MGH Neurological Clinical Research Institute, MGH Deane Institute for Integrated Research on Atrial Fibrillation and Stroke

Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit., National Institutes of Health (U.S.) (Award DP1-OD003646), National Institutes of Health (U.S.) (Award DP2-OD006454), National Institutes of Health (U.S.) (Award K08-GM094394), Burroughs Wellcome Fund (Award 1010625)

Phase-amplitude modulation is a form of cross frequency coupling where the phase of one frequency influences the amplitude of another higher frequency. It has been observed in neurophysiological recordings during sensory, motor, and cognitive tasks, as well as during general anesthesia. In this paper, we describe a novel beamforming procedure to improve estimation of phase-amplitude modulation. We apply this method to 64-channel EEG data recorded during propofol general anesthesia. The method improves the sensitivity of phase-amplitude analyses, and can be applied to a variety of multi-channel neuroscience data where phase-amplitude modulation is present., National Institutes of Health (U.S.) (Grant DP2-OD006454), National Institutes of Health (U.S.) (Grant K25-NS057580), National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant R01-EB006385)

Background: A recent study showed that methylphenidate induces emergence from isoflurane general anesthesia. Isoflurane and propofol are general anesthetics that may have distinct molecular mechanisms of action. The objective of this study was to test the hypothesis that methylphenidate actively induces emergence from propofol general anesthesia. Methods: Using adult rats, the effect of methylphenidate on time to emergence after a single bolus of propofol was determined. The ability of methylphenidate to restore righting during a continuous target-controlled infusion (TCI) of propofol was also tested. In a separate group of rats, a TCI of propofol was established and spectral analysis was performed on electroencephalogram recordings taken before and after methylphenidate administration. Results: Methylphenidate decreased median time to emergence after a single dose of propofol from 735 s (95% CI: 598–897 s, n = 6) to 448 s (95% CI: 371–495 s, n = 6). The difference was statistically significant (P = 0.0051). During continuous propofol anesthesia with a median final target plasma concentration of 4.0 μg/ml (95% CI: 3.2–4.6, n = 6), none of the rats exhibited purposeful movements after injection of normal saline. After methylphenidate, however, all six rats promptly exhibited arousal and had restoration of righting with a median time of 82 s (95% CI: 30–166 s). Spectral analysis of electroencephalogram data demonstrated a shift in peak power from δ (less than 4 Hz) to θ (4–8 Hz) and β (12–30 Hz) after administration of methylphenidate, indicating arousal in 4/4 rats. Conclusions: Methylphenidate decreases time to emergence after a single dose of propofol, and induces emergence during continuous propofol anesthesia in rats. Further study is warranted to test the hypothesis that methylphenidate induces emergence from propofol general anesthesia in humans., National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant K08-GM094394), Massachusetts General Hospital. Dept. of Anesthesia and Critical Care

Hippocampal population codes play an important role in representation of spatial environment and spatial navigation. Uncovering the internal representation of hippocampal population codes will help understand neural mechanisms of the hippocampus. For instance, uncovering the patterns represented by rat hippocampus (CA1) pyramidal cells during periods of either navigation or sleep has been an active research topic over the past decades. However, previous approaches to analyze or decode firing patterns of population neurons all assume the knowledge of the place fields, which are estimated from training data a priori. The question still remains unclear how can we extract information from population neuronal responses either without a priori knowledge or in the presence of finite sampling constraint. Finding the answer to this question would leverage our ability to examine the population neuronal codes under different experimental conditions. Using rat hippocampus as a model system, we attempt to uncover the hidden “spatial topology” represented by the hippocampal population codes. We develop a hidden Markov model (HMM) and a variational Bayesian (VB) inference algorithm to achieve this computational goal, and we apply the analysis to extensive simulation and experimental data. Our empirical results show promising direction for discovering structural patterns of ensemble spike activity during periods of active navigation. This study would also provide useful insights for future exploratory data analysis of population neuronal codes during periods of sleep., National Institutes of Health (U.S.) (NIH Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant MH061976)

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heart beats. We propose a robust algorithm for quantifying instantaneous RSA as applied to heart beat intervals and respiratory recordings under dynamic breathing patterns. The blood volume pressure-derived heart beat series (pulse intervals, PIs) are modeled as an inverse Gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PIs and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated via a frequency domain transfer function evaluated at instantaneous respiratory frequency where high coherence between respiration and PIs is observed. The model is statistically validated using Kolmogorov–Smirnov goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. The presented analysis confirms the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states, reporting statistically significant increase in RSA gain as measured by our paradigm., National Institutes of Health (U.S.) (Grant R01-HL084502), National Institutes of Health (U.S.) (Grant R01-DA015644), National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant K01-AT00694-01)

Coherence analysis characterizes frequency-dependent covariance between signals, and is useful for multivariate oscillatory data often encountered in neuroscience. The global coherence provides a summary of coherent behavior in high-dimensional multivariate data by quantifying the concentration of variance in the first mode of an eigenvalue decomposition of the cross-spectral matrix. Practical application of this useful method is sensitive to noise, and can confound coherent activity in disparate neural populations or spatial locations that have a similar frequency structure. In this paper we describe two methodological enhancements to the global coherence procedure that increase robustness of the technique to noise, and that allow characterization of how power within specific coherent modes change through time., National Institutes of Health (U.S.) (Grant DP2-OD006454), National Institutes of Health (U.S.) (Grant K25-NS057580), National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant R01-EB006385), National Institutes of Health (U.S.) (Grant R01-MH071847)

Accurate quantification of loss of response to external stimuli is essential for understanding the mechanisms of loss of consciousness under general anesthesia. We present a new approach for quantifying three possible outcomes that are encountered in behavioral experiments during general anesthesia: correct responses, incorrect responses and no response. We use a state-space model with two state variables representing a probability of response and a conditional probability of correct response. We show applications of this approach to an example of responses to auditory stimuli at varying levels of propofol anesthesia ranging from light sedation to deep anesthesia in human subjects. The posterior probability densities of model parameters and the response probability are computed within a Bayesian framework using Markov Chain Monte Carlo methods., National Institutes of Health (U.S.) (Grant DP2-OD006454), National Institutes of Health (U.S.) (Grant K25-NS057580), National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant R01-EB006385), National Institutes of Health (U.S.) (Grant R01-MH071847)

The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics.We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5[plus-over-minus sign]0.5 s (mean[plus-over-minus sign]s.d.) . The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25[plus-over-minus sign]3 single units by a factor of 7.0[plus-over-minus sign]0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems., National Institutes of Health (U.S.) (Grant R01 DC009899), National Institutes of Health (U.S.) (Grant RC1 HD063931), National Institutes of Health (U.S.) (Grant N01 HD053403), National Institutes of Health (U.S.) (Grant 5K01 NS057389), National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant R01-EB006385), United States. Dept. of Veterans Affairs (Office of Research and Development, Rehabilitation R&D Service), Massachusetts General Hospital (Deane Institute for Integrated Research on Atrial Fibrillation and Stroke), Doris Duke Charitable Foundation, Spaulding Rehabilitation Hospital

In this article, we present a point process method to assess dynamic baroreflex sensitivity (BRS) by estimating the baroreflex gain as focal component of a simplified closed-loop model of the cardiovascular system. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by linear and bilinear bivariate regressions on both the previous R−R intervals (RR) and blood pressure (BP) beat-to-beat measures. The instantaneous baroreflex gain is estimated as the feedback branch of the loop with a point-process filter, while the RRBP feedforward transfer function representing heart contractility and vasculature effects is simultaneously estimated by a recursive least-squares filter. These two closed-loop gains provide a direct assessment of baroreflex control of heart rate (HR). In addition, the dynamic coherence, cross bispectrum, and their power ratio can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics. To illustrate the application, we have applied the proposed point process model to experimental recordings from 11 healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. We present quantitative results during transient periods, as well as statistical analyses on steady-state epochs before and after propofol administration. Our findings validate the ability of the algorithm to provide a reliable and fast-tracking assessment of BRS, and show a clear overall reduction in baroreflex gain from the baseline period to the start of propofol anesthesia, confirming that instantaneous evaluation of arterial baroreflex control of HR may yield important implications in clinical practice, particularly during anesthesia and in postoperative care., National Institutes of Health (U.S.) (Grant R01-HL084502), National Institutes of Health (U.S.) (Grant K25-NS05758), National Institutes of Health (U.S.) (Grant DP2- OD006454), National Institutes of Health (U.S.) (Grant T32NS048005), National Institutes of Health (U.S.) (Grant R01-DA015644), Massachusetts General Hospital (Clinical Research Center, UL1 Grant RR025758)

Rhythmic local field potentials (LFPs) arise from coordinated neural activity. Inference of neural function based on the properties of brain rhythms remains a challenging data analysis problem. Algorithms that characterize non-stationary rhythms with high temporal and spectral resolution may be useful for interpreting LFP activity on the timescales in which they are generated. We propose a Kalman smoother based dynamic autoregressive model for tracking the instantaneous frequency (iFreq) and frequency modulation (FM) of noisy and non-stationary sinusoids such as those found in LFP data. We verify the performance of our algorithm using simulated data with broad spectral content, and demonstrate its application using real data recorded from behavioral learning experiments. In analyses of ripple oscillations (100–250 Hz) recorded from the rodent hippocampus, our algorithm identified novel repetitive, short timescale frequency dynamics. Our results suggest that iFreq and FM may be useful measures for the quantification of small timescale LFP dynamics., National Institutes of Health (U.S.) (NIH/NIMH R01 MH59733), National Institutes of Health (U.S.) (NIH/NIHLB R01 HL084502), Massachusetts Institute of Technology (Henry E. Singleton Presidential Graduate Fellowship Award)

UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process., National Institutes of Health (U.S.) (Grant R01-DA015644), National Institutes of Health (U.S.) (Director Pioneer Award DP1- OD003646), National Institutes of Health (U.S.) (NIH/NHLBI grant R01-HL084502), National Institutes of Health (U.S.) (NIH institutional NRSA grant T32 HL07901)

Objective. There is growing interest in using closed-loop anesthetic delivery (CLAD) systems to automate control of brain states (sedation, unconsciousness and antinociception) in patients receiving anesthesia care. The accuracy and reliability of these systems can be improved by using as control signals electroencephalogram (EEG) markers for which the neurophysiological links to the anesthetic-induced brain states are well established. Burst suppression, in which bursts of electrical activity alternate with periods of quiescence or suppression, is a well-known, readily discernible EEG marker of profound brain inactivation and unconsciousness. This pattern is commonly maintained when anesthetics are administered to produce a medically-induced coma for cerebral protection in patients suffering from brain injuries or to arrest brain activity in patients having uncontrollable seizures. Although the coma may be required for several hours or days, drug infusion rates are managed inefficiently by manual adjustment. Our objective is to design a CLAD system for burst suppression control to automate management of medically-induced coma. Approach. We establish a CLAD system to control burst suppression consisting of: a two-dimensional linear system model relating the anesthetic brain level to the EEG dynamics; a new control signal, the burst suppression probability (BSP) defining the instantaneous probability of suppression; the BSP filter, a state-space algorithm to estimate the BSP from EEG recordings; a proportional–integral controller; and a system identification procedure to estimate the model and controller parameters. Main results. We demonstrate reliable performance of our system in simulation studies of burst suppression control using both propofol and etomidate in rodent experiments based on Vijn and Sneyd, and in human experiments based on the Schnider pharmacokinetic model for propofol. Using propofol, we further demonstrate that our control system reliably tracks changing target levels of burst suppression in simulated human subjects across different epidemiological profiles. Significance. Our results give new insights into CLAD system design and suggest a control-theory framework to automate second-to-second control of burst suppression for management of medically-induced coma., National Institutes of Health (U.S.) (Award DP1-OD003646), National Institutes of Health (U.S.) (Award R01GM10498), Burroughs Wellcome Fund (Award 1010625)

Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system., National Institutes of Health (U.S.) (NIH grant No.DP1-0D003646-01), National Institutes of Health (U.S.) (NIH grant R01-EB006385)

In functional magnetic resonance imaging (fMRI) studies, a cortical region in the right temporo-parietal junction (RTPJ) is recruited when participants read stories about people's thoughts (‘Theory of Mind’). Both fMRI and lesion studies suggest that a region near the RTPJ is associated with attentional reorienting in response to an unexpected stimulus. Do Theory of Mind and attentional reorienting recruit a single population of neurons, or are there two neighboring but distinct neural populations in the RTPJ? One recent study compared these activations, and found evidence consistent with a single common region. However, the apparent overlap may have been due to the low resolution of the previous technique. We tested this hypothesis using a high-resolution protocol, within-subjects analyses, and more powerful statistical methods. Strict conjunction analyses revealed that the area of overlap was small and on the periphery of each activation. In addition, a bootstrap analysis identified a reliable 6–10 mm spatial displacement between the peak activations of the two tasks; the same magnitude and direction of displacement was observed in within-subjects comparisons. In all, these results suggest that there are neighboring but distinct regions within the RTPJ implicated in Theory of Mind and orienting attention., Ellison Medical Foundation, David and Lucile Packard Foundation, Simons Foundation